基于强化学习的最佳跟踪控制,用于具有不确定的拓网络的不确定的多代理系统.
1The School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
ISA transactions
|December 5, 2024
概括
本研究涉及使用自适应观察者和强化学习 (RL) 的不确定多代理系统 (MAS) 的最佳跟踪控制. 拟议的方法有效地估计了改善控制性能的状态和参数.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理系统 (MAS) 越来越多地应用于智能互联车辆 (ICV) 和无人机 (UAV).
- 对具有不确定的动态和网络拓的MAS进行最佳的跟踪控制仍然是一个重大挑战.
研究的目的:
- 在不确定的网络条件下,为不确定的MAS开发基于观察者的最佳跟踪控制策略.
- 提高MAS在复杂的操作环境中的稳定性和性能.
主要方法:
- 使用并发学习 (CL) 的自适应扩展观察者在放松的激发条件下估计系统状态和未知的参数.
- 一个Luenberger观察员在不确定的拓下补偿领导状态信息.
- 开发了一个基于演员-关键 (AC) 神经网络 (NN) 的最佳跟踪控制算法,消除了对状态衍生信息的需求.
主要成果:
- 同步学习观察者保证估计的未知参数的趋同.
- 建议的观察器有效地弥补了系统不确定性和网络拓变化.
- 演员关键神经网络控制器实现了最佳的跟踪性能,而不需要状态衍生.
结论:
- 综合观察员设计和强化学习方法为在不确定的MAS中实现最佳跟踪控制提供了强大的解决方案.
- 开发的方法通过数值模拟进行验证,证明其对ICV和UAV的有效性.
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